Michael Davies joined NVIDIA in July 2024 and is a member of the Architecture Research Group. He received his Ph.D. from the University of Wisconsin-Madison in 2024.  

Mike's current work focuses on emerging execution models for Deep Learning on GPUs. At a broad level, he is interested in topics spanning architecture, programming languages and operating systems with an eye towards how abstractions at different layers of the technology stack can be crafted to help deliver performance and efficiency by construction – for deep learning and beyond.

Jiefeng Li is a Research Scientist at NVIDIA Research. He received his Ph.D. in the Department of Computer Science at Shanghai Jiao Tong University (SJTU), China, in 2024. He was a member of the SJTU MVIG lab under the supervision of Prof. Cewu Lu. Prior to that, he received his M.E.

My research interests focus on efficient and scalable training for deep learning models. Please check homepage https://lzhu.me/ for latest update.

Shizhe Diao is a research scientist at NVIDIA Learning and Perception Research Group. He completed his Ph.D. at the Hong Kong University of Science and Technology, advised by Professor Tong Zhang. Previously, He was a visiting scholar at BLENDER LAB@UIUC, working with Professor Heng Ji. He was a research intern at ByteDance AI Lab with Dr.

Zhijian Liu (https://zhijianliu.com) is a research scientist at NVIDIA . He finished his PhD at MIT, advised by Prof. Song Han. His research focuses on efficient machine learning and systems. His work has been featured as oral and spotlight presentations at conferences such as NeurIPS, ICLR, and CVPR. He has received the Qualcomm Innovation Fellowship and has been recognized as a Rising Star in ML and Systems by MLCommons and a Rising Star in Data Science by UChicago and UCSD.

Rose Abramson received the Bachelor of Science (B.S.) and Master of Engineering (M.Eng.) degrees in Electrical Engineering from the Massachusetts Institute of Technology in 2015 and 2016, respectively. After graduating, she worked in industry in both the automotive and home-lighting sectors. She received her Ph.D. degree in Electrical Engineering and Computer Science from the University of California, Berkeley, in 2024. During her Ph.D. she interned at NVIDIA in 2022 and at Analog Devices in 2023, working on data center power delivery.

Youssef Elasser received his B.S. degree in Electrical Engineering and Computer Science with a concentration in electric power from Rensselaer Polytechnic Institute in 2018 and received his M.A. and Ph.D. degrees in Electrical and Computer Engineering from Princeton University in 2024. His research interests include power delivery for data center microprocessors, magnetics design and optimization, and dc-dc power conversion. He interned in the NVIDIA Circuits Research Group during the summer of 2023 and joined NVIDIA Research full time in June 2024.